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Phylotype diversity within soil fungal functional groups drives ecosystem stability

Nature Ecology & Evolution volume 6, pages 900–909 (2022)Cite this article

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Abstract

Soil fungi are fundamental to plant productivity, yet their influence on the temporal stability of global terrestrial ecosystems, and their capacity to buffer plant productivity against extreme drought events, remain uncertain. Here we combined three independent global field surveys of soil fungi with a satellite-derived temporal assessment of plant productivity, and report that phylotype richness within particular fungal functional groups drives the stability of terrestrial ecosystems. The richness of fungal decomposers was consistently and positively associated with ecosystem stability worldwide, while the opposite pattern was found for the richness of fungal plant pathogens, particularly in grasslands. We further demonstrated that the richness of soil decomposers was consistently positively linked with higher resistance of plant productivity in response to extreme drought events, while that of fungal plant pathogens showed a general negative relationship with plant productivity resilience/resistance patterns. Together, our work provides evidence supporting the critical role of soil fungal diversity to secure stable plant production over time in global ecosystems, and to buffer against extreme climate events.

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Fig. 1: Relationships between soil fungal diversity and ecosystem stability.
Fig. 2: Relationships between soil fungal diversity and ecosystem stability in grasslands.
Fig. 3: Drivers of ecosystem stability.
Fig. 4: Direct and indirect drivers of ecosystem stability.
Fig. 5: Relationship between basal area of mycorrhizal association and ecosystem stability.
Fig. 6: Relationships between soil fungal diversity and ecosystem resistance and resilience to drought events.

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Data availability

The raw data associated with this study are available at https://figshare.com/s/5299f4b83c1abec736fc (https://doi.org/10.6084/m9.figshare.14905236). ITS sequencing data associated with global surveys 1, 2 and 3 are available at https://figshare.com/s/9772d31625426d90778222 (https://doi.org/10.6084/m9.figshare.5923876.v1), the Short Read Archive (accession SRP043706)23 and https://figshare.com/s/5e16fa5b0475880c0fa5 (https://doi.org/10.6084/m9.figshare.19419335), respectively.

References

  1. Singh, B. K., Bardgett, R. D., Smith, P. & Reay, D. S. Microorganisms and climate change: terrestrial feedbacks and mitigation options. Nat. Rev. Microbiol. 8, 779–790 (2010).

    Article  CAS  PubMed  Google Scholar 

  2. Fierer, N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat. Rev. Microbiol. 15, 579–590 (2017).

    Article  CAS  PubMed  Google Scholar 

  3. Guerra, C. A. et al. Tracking, targeting, and conserving soil biodiversity. Science 371, 239–241 (2021).

    Article  CAS  PubMed  Google Scholar 

  4. Fanin, N. et al. Consistent effects of biodiversity loss on multifunctionality across contrasting ecosystems. Nat. Ecol. Evol. 2, 269–278 (2018).

    Article  PubMed  Google Scholar 

  5. Delgado-Baquerizo, M. et al. Multiple elements of soil biodiversity drive ecosystem functions across biomes. Nat. Ecol. Evol. 4, 210–220 (2020).

    Article  PubMed  Google Scholar 

  6. Chen, W. et al. Fertility-related interplay between fungal guilds underlies plant richness-productivity relationships in natural grasslands. New Phytol. 226, 1129–1143 (2020).

    Article  PubMed  Google Scholar 

  7. Semchenko, M. et al. Fungal diversity regulates plant–soil feedbacks in temperate grassland. Sci. Adv. 4, eaau4578 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Kohli, M. et al. Stability of grassland production is robust to changes in the consumer food web. Ecol. Lett. 22, 707–716 (2019).

    Article  PubMed  Google Scholar 

  9. Liang, M. et al. Soil microbes drive phylogenetic diversity–productivity relationships in a subtropical forest. Sci. Adv. 5, eaax5088 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Tilman, D., Reich, P. B. & Knops, J. M. H. Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature 441, 629–632 (2006).

    Article  CAS  PubMed  Google Scholar 

  11. Yang, G. W., Wagg, C., Veresoglou, S. D., Hempel, S. & Rillig, M. C. How soil biota drive ecosystem stability. Trends Plant Sci. 23, 1057–1067 (2018).

    Article  CAS  PubMed  Google Scholar 

  12. de Vries, F. T., Griffiths, R. I., Knight, C. G., Nicolitch, O. & Williams, A. Harnessing rhizosphere microbiomes for drought-resilient crop production. Science 368, 270–274 (2020).

    Article  PubMed  Google Scholar 

  13. Pörtner, H.O. et al. Scientific outcome of the IPBES-IPCC co-sponsored workshop on biodiversity and climate change (IPBES, 2021).

  14. Gessner, M. O. et al. Diversity meets decomposition. Trends Ecol. Evol. 25, 372–380 (2010).

    Article  PubMed  Google Scholar 

  15. Bardgett, R. D. & van der Putten, W. H. Belowground biodiversity and ecosystem functioning. Nature 515, 505–511 (2014).

    Article  CAS  PubMed  Google Scholar 

  16. Anthony, M. A. et al. Forest tree growth is linked to mycorrhizal fungal composition and function across Europe. ISME J. https://doi.org/10.1038/s41396-021-01159-7 (2022).

  17. Jia, Y. Y., van der Heijden, M. G. A., Wagg, C., Feng, G. & Walder, F. Symbiotic soil fungi enhance resistance and resilience of an experimental grassland to drought and nitrogen deposition. J. Ecol. 109, 3171–3181 (2020).

    Article  Google Scholar 

  18. Delgado-Baquerizo, M. et al. The proportion of soil-borne pathogens increases with warming at the global scale. Nat. Clim. Change 10, 550–554 (2020).

    Article  Google Scholar 

  19. Tedersoo, L., Bahram, M. & Zobel, M. How do mycorrhizal associations drive plant population and community biology? Science 367, eaba1223 (2020).

    Article  CAS  PubMed  Google Scholar 

  20. Guo, X. et al. Climate warming leads to divergent succession of grassland microbial communities. Nat. Clim. Change 8, 813–818 (2018).

    Article  Google Scholar 

  21. Põlme, S. et al. FungalTraits: a user-friendly traits database of fungi and fungus-like stramenopiles. Fungal Divers. 105, 1–16 (2020).

    Article  Google Scholar 

  22. Egidi, E. et al. A few Ascomycota taxa dominate soil fungal communities worldwide. Nat. Commun. 10, 2369 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Tedersoo, L. et al. Global diversity and geography of soil fungi. Science 346, 1078–1088 (2014).

    Article  CAS  Google Scholar 

  24. Delgado-Baquerizo, M. et al. The influence of soil age on ecosystem structure and function across biomes. Nat. Commun. 11, 4721 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Isbell, F. et al. Biodiversity increases the resistance of ecosystem productivity to climate extremes. Nature 526, 574–577 (2015).

    Article  CAS  PubMed  Google Scholar 

  26. Steidinger, B. S. et al. Climatic controls of decomposition drive the global biogeography of forest-tree symbioses. Nature 569, 404–408 (2019).

    Article  CAS  PubMed  Google Scholar 

  27. Wagg, C. et al. Diversity and asynchrony in soil microbial communities stabilizes ecosystem functioning. Elife 10, 3207 (2021).

    Article  Google Scholar 

  28. Yang, G. W., Wagg, C., Veresoglou, S. D., Hempel, S. & Rillig, M. C. Plant and soil biodiversity have non-substitutable stabilizing effects on biomass production. Ecol. Lett. 24, 1582–1593 (2021).

    Article  PubMed  Google Scholar 

  29. Chen, L. T. et al. Above- and belowground biodiversity jointly drive ecosystem stability in natural alpine grasslands on the Tibetan Plateau. Glob. Ecol. Biogeogr. https://doi.org/10.1111/geb.13307 (2021).

  30. Garcia-Palacios, P., Gross, N., Gaitan, J. & Maestre, F. T. Climate mediates the biodiversity-ecosystem stability relationship globally. Proc. Natl Acad. Sci. USA 115, 8400–8405 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Valencia, E. et al. Synchrony matters more than species richness in plant community stability at a global scale. Proc. Natl Acad. Sci. USA 117, 24345–24351 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Craven, D. et al. Multiple facets of biodiversity drive the diversity-stability relationship. Nat. Ecol. Evol. 2, 1579–1587 (2018).

    Article  PubMed  Google Scholar 

  33. Naeem, S. & Li, S. B. Biodiversity enhances ecosystem reliability. Nature 390, 507–509 (1997).

    Article  CAS  Google Scholar 

  34. Hautier, Y. et al. Eutrophication weakens stabilizing effects of diversity in natural grasslands. Nature 508, 521–525 (2014).

    Article  CAS  PubMed  Google Scholar 

  35. Jousset, A., Schmid, B., Scheu, S. & Eisenhauer, N. Genotypic richness and dissimilarity opposingly affect ecosystem performance. Ecol. Lett. 14, 537–624 (2011).

    Article  CAS  PubMed  Google Scholar 

  36. Jiang, L., Pu, Z. & Nemergut, D. R. On the importance of the negative selection effect for the relationship between biodiversity and ecosystem functioning. Oikos 117, 488–493 (2008).

    Article  Google Scholar 

  37. Ratzke, C., Barrere, J. & Gore, J. Strength of species interactions determines biodiversity and stability in microbial communities. Nat. Ecol. Evol. 4, 376–383 (2020).

    Article  PubMed  Google Scholar 

  38. Lekberg, Y. et al. Nitrogen and phosphorus fertilization consistently favor pathogenic over mutualistic fungi in grassland soils. Nat. Commun. 12, 3484 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Bastida, F. et al. Soil microbial diversity–biomass relationships are driven by soil carbon content across global biomes. ISME J. 15, 2081–2091 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Paruelo, J., Epstein, H. E., Lauenroth, W. K. & Burke, I. C. ANPP estimates from NDVI for the central grassland region of the United States. Ecology 78, 953–958 (1997).

    Article  Google Scholar 

  41. Jobbágy, E. G., Sala, O. E. & Paruelo, J. M. Patterns and controls of primary production in the Patagonian steppe: a remote sensing approach. Ecology 83, 307–319 (2002).

    Google Scholar 

  42. Oehri, J., Schmid, B., Schaepman-Strub, G. & Niklaus, P. A. Biodiversity promotes primary productivity and growing season lengthening at the landscape scale. Proc. Natl Acad. Sci. USA 114, 10160–10165 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Bastos, A., Running, S. W., Gouveia, C. & Trigo, R. M. The global NPP dependence on ENSO: La Niña and the extraordinary year of 2011. J. Geophys. Res. Biogeosci. 118, 1247–1255 (2013).

    Article  Google Scholar 

  44. Orwin, K. H. & Wardle, D. A. New indices for quantifying the resistance and resilience of soil biota to exogenous disturbances. Soil Biol. Biochem. 36, 1907–1912 (2004).

    Article  CAS  Google Scholar 

  45. Frankenberg, C. et al. Prospects for chlorophyll fluorescence remote sensing from the Orbiting Carbon Observatory-2. Remote Sens. Environ. 147, 1–12 (2014).

    Article  Google Scholar 

  46. Sun, Y. et al. Overview of solar-induced chlorophyll fluorescence (SIF) from the Orbiting Carbon Observatory-2: retrieval, cross-mission comparison, and global monitoring for GPP. Remote Sens. Environ. 209, 808–823 (2018).

    Article  Google Scholar 

  47. Zhang, Y., Joiner, J., Alemohammad, S. H., Zhou, S. & Gentine, P. A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks. Biogeosciences 15, 5779–5800 (2018).

    Article  CAS  Google Scholar 

  48. Running, S. W. et al. A continuous satellite-derived measure of global terrestrial primary production. Bioscience 54, 547–560 (2004).

    Article  Google Scholar 

  49. Beguería, S. et al. Standardized precipitation evapotranspiration index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. Int. J. Climatol. 34, 3001–3023 (2014).

    Article  Google Scholar 

  50. Chen, C. et al. China and India lead in greening of the world through land-use management. Nat. Sustain. 2, 122–129 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Luo, H. et al. Contrasting responses of planted and natural forests to drought intensity in Yunnan, China. Remote Sens. 8, 635 (2016).

    Article  Google Scholar 

  52. Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations—the CRU TS3. 10 Dataset. Int. J. Climatol. 34, 623–642 (2014).

    Article  Google Scholar 

  53. Allen, R. G. et al. Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements (FAO, 1998); https://www.fao.org/3/x0490e/x0490e00.htm

  54. Oksanen, J. et al. Vegan: Community Ecology Package (R Foundation for Statistical Computing, 2013).

  55. Legendre, P. et al. Studying beta diversity: ecological variation partitioning by multiple regression and canonical analysis. J. Plant Ecol. 1, 3–8 (2008).

    Article  Google Scholar 

  56. Grömping, U. Relative importance for linear regression in R: the package relaimpo. J. Stat. Softw. 17, 1–27 (2006).

    Article  Google Scholar 

  57. Lefcheck., J. S. piecewiseSEM: piecewise structural equation modelling in R for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579.

  58. Bates, D. et al. lme4: linear mixed-effects models using Eigen and S4. J. Stat. Soft. 67, 1–48 (2014).

    Google Scholar 

Download references

Acknowledgements

This project received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 702057 (CLIMIFUN). M.D.-B. was supported by a Ramón y Cajal grant from the Spanish Ministry of Science and Innovation (RYC2018-025483-I). M.D-B. is also supported by a project from the Spanish Ministry of Science and Innovation (PID2020-115813RA-I00), and a project of the Fondo Europeo de Desarrollo Regional (FEDER) and the Consejería de Transformación Económica, Industria, Conocimiento y Universidades of the Junta de Andalucía (FEDER Andalucía 2014-2020 Objetivo temático “01 - Refuerzo de la investigación, el desarrollo tecnológico y la innovación”) associated with the research project P20_00879 (ANDABIOMA). S.L. was supported by the National Natural Science Foundation of China (grant no. 32101491) and fellowship of China Postdoctoral Science Foundation (2021M701968). P.G.-P. was supported by a grant from the Spanish Ministry of Science and Innovation (DUALSOM, PID2020-113021RA-I00). E.G. is supported by the European Research Council grant agreement 647038 (BIODESERT) and the Consellería de Educación, Cultura y Deporte de la Generalitat Valenciana, and the European Social Fund (APOSTD/2021/188).

Author information

Authors and Affiliations

  1. Engineering Research Center of Eco-Environment in Three Gorges Reservoir Region of Ministry of Education, China Three Gorges University, Yichang, China

    Shengen Liu & Dima Chen

  2. Laboratorio de Biodiversidad y Funcionamiento Ecosistémico, Instituto de Recursos Naturales y Agrobiología de Sevilla (IRNAS), CSIC, Seville, Spain

    Shengen Liu & Manuel Delgado-Baquerizo

  3. Huitong Experimental Station of Forest Ecology, CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Shenyang, PR China

    Shengen Liu & Qingkui Wang

  4. Instituto de Ciencias Agrarias, Consejo Superior de Investigaciones Científicas, Madrid, Spain

    Pablo García-Palacios

  5. Mycology and Microbiology Center, University of Tartu, Tartu, Estonia

    Leho Tedersoo

  6. College of Science, King Saud University, Riyadh, Saudi Arabia

    Leho Tedersoo

  7. Multidisciplinary Institute for Environment Studies ‘Ramon Margalef’, University of Alicante, Alicante, Spain

    Emilio Guirado

  8. Andalusian Center for Assessment and Monitoring of Global Change (CAESCG), University of Almeria, Almeria, Spain

    Emilio Guirado

  9. Plant-Soil Interactions Group, Agroscope, Zurich, Switzerland

    Marcel G. A. van der Heijden

  10. Department of Plant and Microbial Biology, University of Zurich, Zurich, Switzerland

    Marcel G. A. van der Heijden

  11. Fredericton Research and Development Centre, Agriculture and Agri-Food Canada, Fredericton, New Brunswick, Canada

    Cameron Wagg

  12. School of Forestry & Landscape Architecture, Anhui Agricultural University, Hefei, China

    Qingkui Wang

  13. Hawkesbury Institute for the Environment, Western Sydney University, Richmond, New South Wales, Australia

    Juntao Wang & Brajesh K. Singh

  14. Global Centre for Land-Based Innovation, Western Sydney University, Penrith, New South Wales, Australia

    Brajesh K. Singh

  15. Unidad Asociada CSIC-UPO (BioFun), Universidad Pablo de Olavide, Seville, Spain

    Manuel Delgado-Baquerizo

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Contributions

M.D.-B. designed the study in consultation with S.L. and P.G.-P. S.L., M.D.-B., L.T. and E.G. analysed the data. S.L. and M.D.-B. wrote the first draft of the paper. P.G.-P., L.T., M.v.d.H., C.W., E.G., D.C., Q.W., J.W. and B.K.S. contributed significantly to improve subsequent drafts.

Corresponding author

Correspondence to Manuel Delgado-Baquerizo.

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Nature Ecology & Evolution thanks Marina Semchenko and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Sampling locations of three global field surveys.

A total of 673 ecosystems were included in this study.

Extended Data Fig. 2 Frequency of drought events (top) and global map of study plot locations (bottom).

The map data is equivalent to the SPEI reclassification in dry and wet events and normal years of 16 August 2018 to illustrate an example of the distribution of events.

Extended Data Fig. 3 Explained variation in ecosystem stability in global survey #1.

Variation partitioning (%) of four categories of predictors (a): climate predictors (V1), soil properties and biomes (V2), fungi (fungal diversity and community composition) (V3) and plant mycorrhizal association (V4) in explaining ecosystem stability, mean and SD NDVI, and ecosystem resistance and resilience to drought events in global survey #1 (n = 235 ecosystems). The values in brackets after each groups present the variance explained.

Extended Data Fig. 4 Explained variation in ecosystem stability in global survey #2.

Variation partitioning (%) of four categories of predictors (a): climate predictors (V1), soil properties and biomes (V2), fungi (fungal diversity and community composition) (V3) and plant mycorrhizal association (V4) in explaining ecosystem stability, mean and SD NDVI, and ecosystem resistance and resilience to drought events in global survey #2 (n = 351 ecosystems). The values in brackets after each groups present the variance explained.

Extended Data Fig. 5 Explained variation in ecosystem stability in global survey #3.

Variation partitioning (%) of four categories of predictors (a): climate predictors (V1), soil properties and biomes (V2), fungi (fungal diversity and community composition) (V3) and plant mycorrhizal association (V4) in explaining ecosystem stability, mean and SD NDVI, and ecosystem resistance and resilience to drought events in global survey #3 (n = 87 ecosystems). The values in brackets after each groups present the variance explained.

Extended Data Fig. 6 Drivers of mean (a) and SD NDVI (b) in global survey #1.

Multiple ranking regression reveal the relative effects of the most important predictors of ecosystem stability (n = 235 ecosystems). The average parameter estimates (standardized regression coefficients) of the model predictors are shown with their associated 95% confidence intervals along with the relative importance of each predictor, expressed as the percentage of explained variance. *P < 0.05, **P < 0.01, ***P < 0.001. Soil saprobe = Soil fungal decomposers.

Extended Data Fig. 7 Drivers of mean (a) and SD NDVI (b) in global survey #2.

Multiple ranking regression reveal the relative effects of the most important predictors of ecosystem stability (a,c) (n = 351 ecosystems). The average parameter estimates (standardized regression coefficients) of the model predictors are shown with their associated 95% confidence intervals along with the relative importance of each predictor, expressed as the percentage of explained variance. *P < 0.05, **P < 0.01, ***P < 0.001. Soil saprobe = Soil fungal decomposers.

Extended Data Fig. 8 Drivers of mean (a) and SD NDVI (b) in global survey #3.

Multiple ranking regression reveal the relative effects of the most important predictors of ecosystem stability (a,c) (n = 87 ecosystems). The average parameter estimates (standardized regression coefficients) of the model predictors are shown with their associated 95% confidence intervals along with the relative importance of each predictor, expressed as the percentage of explained variance. *P < 0.05, **P < 0.01, ***P < 0.001. Soil saprobe = Soil fungal decomposers.

Extended Data Fig. 9 Fitted linear relationships between ecosystem stability and the diversity (richness) of selected functional groups of soil fungi across all ecosystems in global survey #2 (n = 351 ecosystems).

Akaike information criterion (AIC) was used to selected the best model. Significance levels of each predictor are *P < 0.05, **P < 0.01, ***P < 0.001. Grey shade indicates 95% confidence interval. Soil saprobes = soil fungal decomposers. Ecosystem stability was estimated at a resolution of 250 m×250 m. Fungal diversity is estimated at a resolution of 50 m×50 m. Plant diversity was estimated at a resolution of 110 m×110 m.

Extended Data Fig. 10 Explained variation in ecosystem stability in global survey #2.

Variation partitioning (%) of four categories of predictors (a): climate predictors (V1), soil properties and biomes (V2), fungi (fungal diversity and community composition) (V3) and plant richness and mycorrhizal association (V4) in explaining ecosystem stability, mean and SD NDVI, and ecosystem resistance and resilience to drought events in global survey #2 (n = 351 ecosystems). The values in brackets after each groups present the variance explained.

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Liu, S., García-Palacios, P., Tedersoo, L. et al. Phylotype diversity within soil fungal functional groups drives ecosystem stability. Nat Ecol Evol 6, 900–909 (2022). https://doi.org/10.1038/s41559-022-01756-5

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